[Wav2Vec2 Example] Improve fine-tuning script (#14373)

* improve some stuff

* finish

* correct last

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -99,9 +99,24 @@ class ModelArguments:
         metadata={
             "help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
             "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
-            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
+            "vectors will be masked along the time axis."
         },
     )
+    mask_time_length: Optional[int] = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
+        },
+    )
+    mask_feature_length: Optional[int] = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
     layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."})
     ctc_loss_reduction: Optional[str] = field(
         default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
@@ -169,6 +184,10 @@ class DataTrainingArguments:
         default=None,
         metadata={"help": "A list of characters to remove from the transcripts."},
     )
+    eval_metrics: Optional[List[str]] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
     max_duration_in_seconds: Optional[float] = field(
         default=20.0,
         metadata={
@@ -446,6 +465,9 @@ def main():
             "hidden_dropout": model_args.hidden_dropout,
             "final_dropout": model_args.final_dropout,
             "mask_time_prob": model_args.mask_time_prob,
+            "mask_time_length": model_args.mask_time_length,
+            "mask_feature_prob": model_args.mask_feature_prob,
+            "mask_feature_length": model_args.mask_feature_length,
             "gradient_checkpointing": training_args.gradient_checkpointing,
             "layerdrop": model_args.layerdrop,
             "ctc_loss_reduction": model_args.ctc_loss_reduction,
@@ -519,8 +541,8 @@ def main():
     # Let's use word error rate (WER) as our evaluation metric,
     # instantiate a data collator and the trainer
 
-    # Define Metric during training
-    wer_metric = load_metric("wer")
+    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
 
     # for large datasets it is advised to run the preprocessing on a
     # single machine first with ``args.preprocessing_only`` since there will mostly likely
@@ -541,9 +563,9 @@ def main():
         # we do not want to group tokens when computing the metrics
         label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
 
-        wer = wer_metric.compute(predictions=pred_str, references=label_str)
+        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
 
-        return {"wer": wer}
+        return metrics
 
     # Instantiate custom data collator
     data_collator = DataCollatorCTCWithPadding(processor=processor)